CN112590598A - Optimal configuration method and system for mobile charging vehicle - Google Patents

Optimal configuration method and system for mobile charging vehicle Download PDF

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CN112590598A
CN112590598A CN202011453519.7A CN202011453519A CN112590598A CN 112590598 A CN112590598 A CN 112590598A CN 202011453519 A CN202011453519 A CN 202011453519A CN 112590598 A CN112590598 A CN 112590598A
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mobile charging
node
vehicle
charging vehicle
mobile
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CN112590598B (en
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周开乐
刘璐
陆信辉
丁涛
杨善林
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Hefei University of Technology
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Abstract

The invention provides a mobile charging vehicle optimal configuration method and system, and relates to the technical field of new energy vehicles. The technical scheme includes that firstly, multiple groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles are obtained; then, acquiring an optimized scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimized scheduling model; and solving the optimized scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result. The technical scheme effectively solves the problem of optimal configuration of the mobile charging car, has strong adaptability to flexible and changeable charging requirements of the electric car, and can improve the economical efficiency and effectiveness of the operation of the mobile charging car.

Description

Optimal configuration method and system for mobile charging vehicle
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a mobile charging vehicle optimal configuration method and system.
Background
In recent years, although the electric automobile industry develops rapidly, the development speed of the electric automobile industry is still limited by the limitation of the energy storage battery technology, and the endurance mileage is still one of important factors influencing the popularization of electric automobiles. At present, electric automobile mainly charges through fixed charging station, but fixed charging station construction cycle is long, investment cost is high, the flexibility is relatively poor. Compared with a fixed charging station, the mobile charging vehicle adopts a form of receiving charging demand information in advance and serving at home, and can adapt to various changes of charging demands more flexibly. In order to reduce the cost better and realize greater profit, the mobile charging vehicle needs to be optimized. Currently, the optimization of the mobile charging vehicle includes two aspects of the optimization of the scheduling and the optimization of the configuration of the mobile charging vehicle.
The existing research on optimizing the mobile charging vehicle mostly focuses on optimizing and scheduling the mobile charging vehicle and converts the problem into a path planning problem of the electric vehicle with a time window, and the research is to optimize the operation strategy of the mobile charging vehicle on the basis of determining the configuration of the mobile charging vehicle; the research on the optimal configuration of the fixed charging station is based on the economy of both fixed charging station operators and electric vehicles or the economy of a power grid, and optimizes the site selection, the volume fixing, the charging pile quantity, the matching of renewable energy sources and the feasibility of energy storage and the like of the charging station by combining charging demand prediction data, charging historical data, geographical planning information and the like.
Therefore, the optimization research of the current mobile charging vehicle focuses more on the optimization of the operation strategy of the mobile charging vehicle, and the optimization configuration is not considered; due to the fact that the fixed charging station is poor in mobility, large in scale and fixed in charging requirement, the method for optimizing configuration of the fixed charging station cannot be fully suitable for the characteristics that a mobile charging vehicle is high in mobility, the requirements on the size and the quality of energy storage equipment are high, the charging requirement is random to a certain degree, and the like. In conclusion, the prior art cannot optimize the configuration of the mobile charging vehicle.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a mobile charging vehicle optimal configuration method and system, and solves the problem that the prior art cannot optimize the configuration of the mobile charging vehicle.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention first provides a mobile charging vehicle optimal configuration method, where the method includes:
acquiring multiple groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
acquiring an optimized scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimized scheduling model;
and solving the optimized scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data includes: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
Preferably, the objective function of the optimized scheduling model is as follows:
max profit＝I-C1-C2-C3-C4
wherein I represents a mobile chargerTotal revenue for tram operators; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
Preferably, the total income I of the mobile charging vehicle operator is as follows:
Figure BDA0002832441460000021
dispatch cost C of mobile charging vehicle1：
Figure BDA0002832441460000031
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure BDA0002832441460000032
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure BDA0002832441460000033
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure BDA0002832441460000034
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the node indicates that the ith mobile charging vehicle does not access the nodej；αi,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; x is the number ofiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle is not dispatched; epsiloniA dispatch cost for the ith mobile charging cart; q is the total number of fixed charging stations;
Figure BDA0002832441460000035
for making a decision on a variable, when
Figure BDA0002832441460000036
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000037
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure BDA0002832441460000041
Is a soft time window, a presentation sectionThe optimal time interval for the point j to expect the mobile charging vehicle i to arrive; outer time window
Figure BDA0002832441460000042
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure BDA0002832441460000043
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure BDA0002832441460000044
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure BDA0002832441460000045
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure BDA0002832441460000046
Figure BDA0002832441460000047
Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of the node:
Figure BDA0002832441460000048
arrival time constraint of the mobile charging vehicle:
Figure BDA0002832441460000049
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure BDA00028324414600000410
the electric automobile receives service constraint:
Figure BDA00028324414600000411
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure BDA0002832441460000051
and (3) remaining power range constraint of the mobile charging vehicle:
Figure BDA0002832441460000052
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure BDA0002832441460000053
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure BDA0002832441460000054
Figure BDA0002832441460000055
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure BDA0002832441460000056
the time when the mobile charging vehicle i reaches the node j +1 is the time;
Figure BDA0002832441460000057
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;rkdischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure BDA0002832441460000058
for making a decision on a variable, when
Figure BDA0002832441460000059
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA00028324414600000510
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure BDA00028324414600000511
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure BDA0002832441460000061
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to fail to be in the corresponding time intervalInternal arrival; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,When j is 1, the access node j of the ith mobile charging vehicle is shown, and when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
In a second aspect, the present invention provides a mobile charging vehicle optimal configuration system, including:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
the model acquisition module is used for acquiring an optimized scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimized scheduling model;
and the configuration scheme determining module is used for solving the optimized scheduling model by using the configuration data and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data acquired by the data acquisition module includes: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
Preferably, the objective function of the optimized scheduling model constructed by the model obtaining module is as follows:
max profit＝I-C1-C2-C3-C4
wherein I represents the total income of the mobile charging vehicle operator; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
Preferably, the total income I of the mobile charging vehicle operator is as follows:
Figure BDA0002832441460000071
dispatch cost C of mobile charging vehicle1：
Figure BDA0002832441460000072
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure BDA0002832441460000073
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure BDA0002832441460000074
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure BDA0002832441460000075
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith vehicle is representedThe mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; x is the number ofiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle is not dispatched; epsiloniA dispatch cost for the ith mobile charging cart; q is the total number of fixed charging stations;
Figure BDA0002832441460000078
for making a decision on a variable, when
Figure BDA0002832441460000076
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000077
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure BDA0002832441460000081
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure BDA0002832441460000082
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure BDA0002832441460000083
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure BDA0002832441460000084
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure BDA0002832441460000085
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure BDA0002832441460000086
Figure BDA0002832441460000087
Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of the node:
Figure BDA0002832441460000088
arrival time constraint of the mobile charging vehicle:
Figure BDA0002832441460000089
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure BDA0002832441460000091
the electric automobile receives service constraint:
Figure BDA0002832441460000092
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure BDA0002832441460000093
and (3) remaining power range constraint of the mobile charging vehicle:
Figure BDA0002832441460000094
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure BDA0002832441460000095
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure BDA0002832441460000096
Figure BDA0002832441460000097
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure BDA0002832441460000098
the time when the mobile charging vehicle i reaches the node j +1 is the time;
Figure BDA0002832441460000099
for moving charging vehiclesi time when it reaches node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1 is a decision variable, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure BDA00028324414600000910
for making a decision on a variable, when
Figure BDA00028324414600000911
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA00028324414600000912
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure BDA0002832441460000101
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure BDA0002832441460000102
Reaching node j within a time interval; when the decision variable is taken to be 0, the shift is respectively representedThe mobile charging vehicle i cannot arrive within a corresponding time interval; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
(III) advantageous effects
The invention provides a mobile charging vehicle optimal configuration method and system. Compared with the prior art, the method has the following beneficial effects:
according to the method, the optimal configuration scheme of the mobile charging vehicle is determined by acquiring multiple groups of configuration data of the mobile charging vehicle, constructing the optimal scheduling model of the mobile charging vehicle based on the charging demand information data and the node information data of the mobile charging vehicle, bringing the multiple groups of acquired configuration data of the mobile charging vehicle into the optimal scheduling model for operation and solving, and finally acquiring accurate data of optimal configuration. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and variable charging requirements of the electric vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an optimal configuration method for a mobile charging vehicle according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a mobile charging vehicle optimal configuration method and system, solves the problem that the mobile charging vehicle cannot be optimally configured in the prior art, and achieves the purpose of improving the operation economy and effectiveness of the mobile charging vehicle.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in order to solve the problem that the configuration of the mobile charging vehicle cannot be optimized in the prior art, the technical scheme includes that a plurality of groups of mobile charging vehicle configuration data are obtained firstly, an optimized scheduling model is built according to the total income and various costs of the mobile charging vehicle, the plurality of groups of mobile charging vehicle configuration data are substituted into the optimized scheduling model for optimized scheduling, and finally the corresponding group of mobile charging vehicle configuration data when the optimized scheduling model obtains an optimal scheduling result are used as the optimal configuration of the mobile charging vehicle.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
in a first aspect, the present invention first provides a mobile charging vehicle optimal configuration method, including:
s1, acquiring multiple groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
s2, acquiring an optimized scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimized scheduling model;
and S3, solving the optimized scheduling model by using the configuration data, and determining an optimal configuration scheme based on the solution result.
Therefore, according to the method, the optimal configuration scheme of the mobile charging vehicle is determined by obtaining a plurality of groups of configuration data of the mobile charging vehicle, constructing the optimal scheduling model of the mobile charging vehicle based on the charging demand information data and the node information data of the mobile charging vehicle, bringing the obtained plurality of groups of configuration data of the mobile charging vehicle into the optimal scheduling model for operation and solution, and finally obtaining the accurate data of the optimal configuration. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and variable charging requirements of the electric vehicle.
In the foregoing method according to the embodiment of the present invention, the acquired configuration data includes: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
In addition, in the embodiment of the present invention, in order to obtain the optimal configuration of the mobile charging vehicle, a preferable processing manner is to construct an optimal scheduling model from the consideration of the maximum total profit of the mobile charging vehicle operator, where an objective function of the optimal scheduling model is:
max profit＝I-C1-C2-C3-C4
wherein I represents the total income of the mobile charging vehicle operator; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
In practice, the items of data for constructing the optimized scheduling model include: the total income I of the mobile charging vehicle operator is as follows:
Figure BDA0002832441460000121
dispatch cost C of mobile charging vehicle1：
Figure BDA0002832441460000122
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure BDA0002832441460000123
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure BDA0002832441460000131
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure BDA0002832441460000132
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; x is the number ofiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle is not dispatched; epsiloniA dispatch cost for the ith mobile charging cart; q is fixed charging station assemblyCounting;
Figure BDA0002832441460000133
for making a decision on a variable, when
Figure BDA0002832441460000134
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000135
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure BDA0002832441460000136
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure BDA0002832441460000141
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure BDA0002832441460000142
for mobile chargingThe time when trolley i reaches node j;
Figure BDA0002832441460000143
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure BDA0002832441460000144
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure BDA0002832441460000145
Figure BDA0002832441460000146
Penalty cost per unit time when node j is reached.
In addition, in the embodiment of the present invention, in order to fully consider the possible limitations of the mobile charging vehicle in the actual operation to avoid the influence of the limitations on the optimization result, a preferred processing manner is that the set constraint conditions include: the constraint conditions include: time window constraint of the node:
Figure BDA0002832441460000147
arrival time constraint of the mobile charging vehicle:
Figure BDA0002832441460000148
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure BDA0002832441460000149
the electric automobile receives service constraint:
Figure BDA00028324414600001410
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure BDA00028324414600001411
and (3) remaining power range constraint of the mobile charging vehicle:
Figure BDA0002832441460000151
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure BDA0002832441460000152
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure BDA0002832441460000153
Figure BDA0002832441460000154
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure BDA0002832441460000155
the time when the mobile charging vehicle i reaches the node j +1 is the time;
Figure BDA0002832441460000156
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure BDA0002832441460000157
for making a decision on a variable, when
Figure BDA0002832441460000158
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000159
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure BDA00028324414600001510
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure BDA00028324414600001511
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the node indicates that the ith mobile charging vehicle does not access the nodej; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
The following describes the implementation of an embodiment of the present invention in detail with reference to the explanation of specific steps.
Fig. 1 is a flowchart of an optimal configuration method for a mobile charging vehicle according to the present invention, and referring to fig. 1, a specific process of the optimal configuration method for the mobile charging vehicle includes:
and S1, acquiring multiple groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicle.
In combination with the market condition of the mobile charging vehicle, as many groups of mobile charging vehicle configuration data as possible are obtained from mobile charging vehicle manufacturers. The configuration data includes battery capacity configuration data and fleet configuration data, wherein the battery capacity configuration data represents the battery capacity C of each group of mobile charging vehiclesMThe fleet configuration data represents the number n of different types of mobile charging vehicles1、n2、n3. Wherein n is1、n2、n3Respectively represents the number of mobile charging vehicles which can only provide low-power charging service, the number of mobile charging vehicles which can only provide high-power charging service and the number of mobile charging vehicles which can provide high-power charging service and low-power charging service.
Acquiring the charging requirement information of the electric vehicle in the future 24 hours, wherein the charging requirement information of the electric vehicle comprises a preset charging position LN, a required electric quantity RN, a time window TW and a charging mode MN. Determining node information to be visited by a mobile charging vehicle, determining the position of a central station and the position of a fixed charging station, converting an electric vehicle and the central station into nodes to be visited to form m nodes, wherein the nodes with the number of 1 and the number of m are set as the central station, the nodes with the number of 2-m-1 are set as the electric vehicle with a charging demand, and the electric vehicles are respectively numbered as 2-m-1 according to the sequence of acquiring the charging demand information. The fixed charging stations are individually numbered from 1 to q.
Taking node j as an example, the predetermined charging position coordinate of node j is (LNx)j,LNyj) The charging demand is RNjThe inner layer time window is
Figure BDA0002832441460000161
The outer time window is
Figure BDA0002832441460000162
The selected charging mode is MNjThen, the charging requirement information of all nodes can be expressed as:
charging position information:
Figure BDA0002832441460000171
the required electric quantity information:
RN＝[RN1 … RNj … RNm]T
time window information:
Figure BDA0002832441460000172
charging mode information:
MN＝[MN1 … MNj … MNm]T。
and S2, acquiring an optimized scheduling model based on the charging demand information data and the node information data, and determining the constraint conditions of the optimized scheduling model.
The total income of the mobile charging car operator is equal to the charging service fee and the electricity fee, and the model formula can be expressed as follows:
Figure BDA0002832441460000173
wherein n is the total number of the mobile charging cars, and n is equal to n1+n2+n3M is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jIn order to decide whether the mobile charging vehicle i accesses the decision variable of the node j, when xi,jWhen the value is 1, the ith mobile charging vehicle is accessed to the node j; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i, alpha, according to the type and charging mode of the mobile charging vehiclei,kValues are also different, wherein k is 1, and 2 respectively represents a low-power charging mode and a high-power charging mode; RN (radio network node)jIs the required electric quantity of the node j; p is a radical ofEThe unit price of electricity for the mobile charging vehicle to charge the electric vehicle. Since the mobile charging cart starts from the central station and returns to the central station after all services are finished, both the start node and the end node of the access sequence are the central station, and the central station does not need to be charged by the mobile charging cart, the required charging capacity is 0 when the node is the central station, and for example, when the mobile charging cart accesses node 1(j is 1) and node m (j is m), the required charging capacity of the node is 0.
The various costs of the mobile charging vehicle include: the mobile charging vehicle dispatch cost is the cost of dispatching the mobile charging vehicle, and comprises the use cost and the driver salary of the mobile charging vehicle, and the dispatch cost of one mobile charging vehicle is represented by a fixed value.
Figure BDA0002832441460000181
Wherein n is the total number of the mobile charging vehicles; x is the number ofiA decision variable for deciding whether to dispatch the ith mobile charging vehicle, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle is not dispatched; epsiloniDispatching of charging cars for ith vehicle movementThe method comprises the use cost and the driver salary of the mobile charging car, and epsilon is different according to the type of the mobile charging cariThe values are different, and the values do not change along with the running time, the running distance and the like.
The travel costs of the mobile charging vehicle to and from the stationary charging station include travel costs for traveling from the electric vehicle's current location to the selected stationary charging station and traveling from the stationary charging station to the next node in the access sequence.
Figure BDA0002832441460000182
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j;
Figure BDA0002832441460000183
a decision variable for deciding whether the mobile charging vehicle i goes to the fixed charging station to supplement the electric energy after accessing the node j, when the decision variable is equal to the decision variable
Figure BDA0002832441460000184
When the value is 1, the mobile charging vehicle i is shown to go to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000185
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zA decision variable for deciding whether the mobile charging vehicle goes to the fixed charging station z to supplement the electric energy after accessing the node j is determined when omegaj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMThe unit price of electricity for the fixed charging station to charge the mobile charging vehicle.
The service cost of charging the electric vehicle by the mobile charging vehicle comprises the driving cost of driving to each node in the access sequence, the electric energy cost of charging the electric vehicle and the loss cost of the battery of the mobile charging vehicle.
Figure BDA0002832441460000191
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; RN (radio network node)jRepresenting the required electric quantity of the node j; cMThe battery capacity of the mobile charging car; t isMThe total cycle number in the life cycle of the mobile charging vehicle battery is obtained; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery when the mobile charging vehicle discharges in the k charging service mode is obtained.
The penalty cost of the mobile charging vehicle violating the time window is the penalty cost when the time of the mobile charging vehicle arriving at the node violates the node time window, and when the mobile charging vehicle arrives in the node soft time window, the penalty cost violating the time window does not exist; the cost comprises two types of waiting cost arriving earlier than a time window and arriving cost arriving later than the time window, and the unit time punishment cost arriving in different time intervals is different.
Figure BDA0002832441460000192
Wherein, the inner layer time window
Figure BDA0002832441460000193
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure BDA0002832441460000201
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure BDA0002832441460000202
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure BDA0002832441460000203
the decision variables for deciding the time interval of the mobile charging vehicle i when reaching the node j indicate that the mobile charging vehicle i is in the state when the values of the decision variables are 1
Figure BDA0002832441460000204
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure BDA0002832441460000205
Penalty cost per unit time when node j is reached; when the charging vehicle is moved
Figure BDA0002832441460000206
When the order is cancelled, the electric automobile cancels the order, and the punishment cost of the late arrival of the mobile charging automobile is the due income of the order; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; alpha is alphai,kCharging service fee for the mobile charging vehicle i, alpha, according to the type and charging mode of the mobile charging vehiclei,kThe values are also different, where k is 1,2 denotes low power charging mode anda high power charging mode; RN (radio network node)jIs the required power of the node j.
In summary, the total profit of the mobile charging vehicle can be expressed as:
max profit＝I-C1-C2-C3-C4。
the final purpose of the technical scheme is to obtain the battery capacity configuration data when the total profit of the mobile charging vehicle is the maximum and the configuration data of different types of vehicle proportions, so that the formula is used as an objective function of an optimized dispatching model.
Determining a constraint condition of an objective function of the optimized scheduling model specifically includes:
time window constraint of the node: the optimal scheduling of the mobile charging vehicle is static optimal scheduling in the day, so the time windows of all the access nodes are in the future 24 hours, and the optimal scheduling can be expressed as follows:
Figure BDA0002832441460000207
wherein the content of the first and second substances,
Figure BDA0002832441460000208
respectively the earliest and latest time points of the outer time window of the node j,
Figure BDA0002832441460000209
Figure BDA0002832441460000211
respectively the earliest and latest time points of the inner layer time window of the node j; when the node is a central station, the inner and outer time windows are [0,24 ]]I.e. by
Figure BDA0002832441460000212
And is
Figure BDA0002832441460000213
Figure BDA0002832441460000214
Arrival time constraint of the mobile charging vehicle: the time when the mobile charging vehicle arrives at the node j +1 is the time when the mobile charging vehicle arrives at the node j plus the traveling time when the mobile charging vehicle serves the node j and travels to the node j +1, or plus the sum of the charging time when the mobile charging vehicle previously supplements electric energy to the fixed charging station z and the traveling time when the mobile charging vehicle travels from the fixed charging station z to the node j +1, and can be expressed as follows:
Figure BDA0002832441460000215
wherein the content of the first and second substances,
Figure BDA0002832441460000216
at the time when the mobile charging vehicle i arrives at the node j +1,
Figure BDA0002832441460000217
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j; RN (radio network node)jRepresenting the required electric quantity of the node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure BDA0002832441460000218
for making a decision on a variable, when
Figure BDA0002832441460000219
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA00028324414600002110
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zBecome a decisionAmount when ω isj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; r isMAnd charging power of the mobile charging vehicle at a fixed charging station.
The arrival time of the mobile charging vehicle is in an interval constraint: the interval of the time when the mobile charging vehicle reaches the node is unique and is expressed by a formula as follows:
Figure BDA0002832441460000221
wherein λ isi,j、
Figure BDA0002832441460000222
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure BDA0002832441460000223
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to fail to arrive in the corresponding time interval. When the mobile charging vehicle is in the inner time window
Figure BDA0002832441460000224
When the node j is reached internally, the time window is not violated, so the penalty cost of violating the time window is not generated.
The electric automobile receives service constraint: all nodes except the central node can only be served by one mobile charging vehicle, and the formula is as follows:
Figure BDA0002832441460000225
wherein n is the total number of the mobile charging vehicles; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j;
the charge and discharge quantity of the mobile charging vehicle is restricted: if the mobile charging vehicle supplements the electric energy on the way, the sum of the initial electric quantity and the supplemented electric quantity on the way is equal to the total electric quantity consumed by completing all the services and returning to the central station; if the mobile charging vehicle does not supplement electric energy on the way, the initial electric quantity is not less than the total electric quantity consumed by the mobile charging vehicle, and the formula is represented as follows:
Figure BDA0002832441460000226
wherein m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; RN (radio network node)jRepresenting the required electric quantity of the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1;
Figure BDA0002832441460000231
for making a decision on a variable, when
Figure BDA0002832441460000232
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000233
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; cMThe battery capacity of the mobile charging car;
and (3) remaining power range constraint of the mobile charging vehicle: the residual capacity of the battery of the mobile charging vehicle is always larger than zero and smaller than the capacity of the battery of the mobile charging vehicle, and is expressed by a formula:
Figure BDA0002832441460000234
wherein m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; cMThe battery capacity of the mobile charging car; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; RN (radio network node)jRepresenting the required electric quantity of the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle;
Figure BDA0002832441460000235
for making a decision on a variable, when
Figure BDA0002832441460000236
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000241
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1;
electric automobile demand electric quantity restraint: the charging demand electric quantity at the node is greater than 0 and smaller than the battery capacity of the electric automobile at the node; the battery capacity of the central site is 0.
0≤RNj≤CE
Wherein, RNjRepresenting the required electric quantity of the node j; cEIndicating the battery capacity of the mobile charging vehicle.
And S3, solving the optimized scheduling model by using the configuration data, and determining an optimal configuration scheme based on the solution result.
And respectively substituting the acquired various groups of mobile charging vehicle configuration data into the optimized scheduling model, then carrying out optimized scheduling based on an objective function of the optimized scheduling model, determining a group of mobile charging vehicle configuration data which enables the total profit of a mobile charging vehicle operator to be the maximum as an optimal configuration scheme, and feeding back the optimal configuration scheme (including the optimal configuration of battery capacity, the optimal quantity ratio of mobile charging vehicles which can provide high-power charging service, low-power charging service and high-power and low-power charging service) to the mobile charging vehicle operator.
Example 2:
in a second aspect, the present invention further provides a mobile charging vehicle optimal configuration system, including:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
the model acquisition module is used for acquiring an optimized scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimized scheduling model;
and the configuration scheme determining module is used for solving the optimized scheduling model by using the configuration data and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data acquired by the data acquisition module includes: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
Preferably, the objective function of the optimized scheduling model constructed by the model obtaining module is as follows:
max profit＝I-C1-C2-C3-C4
wherein I represents the total income of the mobile charging vehicle operator; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
Preferably, the total income I of the mobile charging vehicle operator is as follows:
Figure BDA0002832441460000251
dispatch cost C of mobile charging vehicle1：
Figure BDA0002832441460000252
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure BDA0002832441460000253
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure BDA0002832441460000254
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure BDA0002832441460000255
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; x is the number ofiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, no dispatch is indicatediA vehicle mobile charging vehicle; epsiloniA dispatch cost for the ith mobile charging cart; q is the total number of fixed charging stations;
Figure BDA0002832441460000261
for making a decision on a variable, when
Figure BDA0002832441460000262
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000263
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj，zA value of 1 indicates being stationarySupplementing electric energy at a charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure BDA0002832441460000264
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure BDA0002832441460000265
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure BDA0002832441460000266
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure BDA0002832441460000267
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure BDA0002832441460000268
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure BDA0002832441460000269
Figure BDA0002832441460000271
Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of the node:
Figure BDA0002832441460000272
arrival time constraint of the mobile charging vehicle:
Figure BDA0002832441460000273
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure BDA0002832441460000274
the electric automobile receives service constraint:
Figure BDA0002832441460000275
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure BDA0002832441460000276
and (3) remaining power range constraint of the mobile charging vehicle:
Figure BDA0002832441460000277
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure BDA0002832441460000278
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure BDA0002832441460000279
Figure BDA00028324414600002710
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure BDA00028324414600002711
the time when the mobile charging vehicle i reaches the node j +1 is the time;
Figure BDA00028324414600002712
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure BDA0002832441460000281
for making a decision on a variable, when
Figure BDA0002832441460000282
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure BDA0002832441460000283
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging work for mobile charging vehicle in fixed charging stationRate; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure BDA0002832441460000284
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure BDA0002832441460000285
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
It can be understood that the mobile charging vehicle optimal configuration system provided in the embodiment of the present invention corresponds to the mobile charging vehicle optimal configuration method, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the mobile charging vehicle optimal configuration method, which are not described herein again.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the method comprises the steps of obtaining a plurality of groups of mobile charging car configuration data, constructing an optimal scheduling model of the mobile charging car based on charging demand information data and node information data of the mobile charging car, substituting the obtained plurality of groups of mobile charging car configuration data into the optimal scheduling model for operation and solving, and finally obtaining accurate data of optimal configuration, thereby determining an optimal mobile charging car configuration scheme. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and variable charging requirements of the electric vehicle;
2. the invention simultaneously optimizes the battery capacity configuration and the fleet ratio configuration of the mobile charging vehicle, thereby not only reducing the high cost of the mobile charging vehicle caused by overhigh battery capacity, but also effectively meeting different charging requirements of the electric vehicle due to the reasonable ratio of the number of the mobile charging vehicles;
3. in the process of carrying out optimized scheduling on the mobile charging vehicle optimized scheduling model, the soft time window and the hard time window are set simultaneously, so that the penalty cost of violating the time window by the mobile charging vehicle is fully considered, and the optimization result is more accurate.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A mobile charging vehicle optimal configuration method is characterized by comprising the following steps:
acquiring multiple groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
acquiring an optimized scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimized scheduling model;
and solving the optimized scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result.
2. The method of claim 1, wherein the configuration data comprises: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
3. The method of claim 1, wherein the objective function of the optimized scheduling model is:
max profit＝I-C1-C2-C3-C4
wherein I represents the total income of the mobile charging vehicle operator; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
4. The method of claim 3, wherein the mobile charging cart operator total revenue I:
Figure FDA0002832441450000011
dispatch cost C of mobile charging vehicle1：
Figure FDA0002832441450000012
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure FDA0002832441450000021
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure FDA0002832441450000022
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure FDA0002832441450000023
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging electric automobile by mobile charging car；xiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle epsilon is not dispatchedi(ii) a A dispatch cost for the ith mobile charging cart; q is the total number of fixed charging stations;
Figure FDA0002832441450000024
for making a decision on a variable, when
Figure FDA0002832441450000025
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure FDA0002832441450000026
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure FDA0002832441450000031
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure FDA0002832441450000032
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure FDA0002832441450000033
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure FDA0002832441450000034
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure FDA0002832441450000035
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure FDA0002832441450000036
Figure FDA0002832441450000037
Penalty cost per unit time when node j is reached.
5. The method of claim 4, wherein the constraints comprise:
time window constraint of the node:
Figure FDA0002832441450000038
arrival time constraint of the mobile charging vehicle:
Figure FDA0002832441450000039
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure FDA00028324414500000310
the electric automobile receives service constraint:
Figure FDA00028324414500000311
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure FDA0002832441450000041
and (3) remaining power range constraint of the mobile charging vehicle:
Figure FDA0002832441450000042
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure FDA0002832441450000043
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure FDA0002832441450000044
Figure FDA0002832441450000045
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure FDA0002832441450000046
for moving charging vehicle i arriving nodeTime at point j + 1;
Figure FDA0002832441450000047
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure FDA0002832441450000048
for making a decision on a variable, when
Figure FDA0002832441450000049
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure FDA00028324414500000410
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1The distance between the mobile charging vehicle i and the node j +1 is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure FDA00028324414500000411
Are all decision variables, when lambdai,jValue of 1, respectively tableShow the mobile charging car i is
Figure FDA00028324414500000412
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
6. A mobile charging cart optimized configuration system, the system comprising:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
the model acquisition module is used for acquiring an optimized scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimized scheduling model;
and the configuration scheme determining module is used for solving the optimized scheduling model by using the configuration data and determining an optimal configuration scheme based on a solving result.
7. The system of claim 6, wherein the configuration data obtained by the data obtaining module comprises: battery capacity configuration data and fleet ratio configuration data; the charging demand information data includes: the method comprises the steps of presetting a charging position, required electric quantity, a time window and charging mode data; the node information data includes: the system comprises electric vehicle position data, central station position data and fixed charging station position data.
8. The system of claim 6, wherein the model acquisition module constructs the optimal scheduling model with an objective function of:
max profit＝I-C1-C2-C3-C4
wherein I represents the total income of the mobile charging vehicle operator; c1Represents the mobile charging car dispatch cost; c2Representing the running cost of the mobile charging vehicle to and from the fixed charging station; c3The service cost of the mobile charging vehicle for charging the electric vehicle is represented; c4Represents the penalty cost of the mobile charging vehicle violating the time window.
9. The system of claim 8, wherein the mobile charging cart operator total revenue I:
Figure FDA0002832441450000061
dispatch cost C of mobile charging vehicle1：
Figure FDA0002832441450000062
Running cost C of mobile charging vehicle to and from fixed charging station2：
Figure FDA0002832441450000063
Service cost C for charging electric automobile by using mobile charging vehicle3：
Figure FDA0002832441450000064
Penalty cost C of violation of time window of mobile charging vehicle4：
Figure FDA0002832441450000065
Wherein n is the total number of the mobile charging vehicles; m is the total number of nodes of the electric automobile and the nodes of the central station; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented; when x isi,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; alpha is alphai,kCharging service fee for the mobile charging vehicle i; k represents a charging mode; RN (radio network node)jRepresenting the required electric quantity of the node j; p is a radical ofEUnit electricity price for charging the electric vehicle by the mobile charging vehicle; x is the number ofiFor decision variables, when xiWhen the value is 1, dispatching the ith mobile charging vehicle; when x isiWhen the value is 0, the ith mobile charging vehicle is not dispatched; epsiloniA dispatch cost for the ith mobile charging cart; q is the total number of fixed charging stations;
Figure FDA0002832441450000066
for making a decision on a variable, when
Figure FDA0002832441450000067
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure FDA0002832441450000068
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zA value of 0 indicates not being fixedSupplementing electric energy at a fixed charging station z; dz,j+1The distance between the fixed charging station z and the node j + 1; theta is the unit mileage energy consumption of the mobile charging vehicle; p is a radical ofMUnit electricity price for charging the mobile charging car for the fixed charging station; di,jThe distance between the mobile charging vehicle i and the node j is obtained; cMThe battery capacity of the mobile charging car; t isMThe total cycle number of the mobile charging vehicle in the life cycle of the battery is calculated; gamma is the replacement cost of the mobile charging vehicle battery; mu.skThe loss coefficient of the self battery is the loss coefficient of the mobile charging vehicle when the mobile charging vehicle discharges in the k charging service mode; inner time window
Figure FDA0002832441450000071
The time window is a soft time window and represents the optimal time interval for the node j to expect the mobile charging vehicle i to arrive; outer time window
Figure FDA0002832441450000072
A hard time window represents the maximum time interval for the mobile charging vehicle i to reach which the node j can accept;
Figure FDA0002832441450000073
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j;
Figure FDA0002832441450000074
are decision variables, and when the values of the decision variables are 1, the decision variables respectively indicate that the mobile charging vehicle i is in
Figure FDA0002832441450000075
Reaches the node j within a certain time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; c. Ce、cl、cminAre respectively a mobile charging vehicle
Figure FDA0002832441450000076
Figure FDA0002832441450000077
Penalty cost per unit time when node j is reached.
10. The system of claim 9, wherein the constraints comprise: time window constraint of the node:
Figure FDA0002832441450000078
arrival time constraint of the mobile charging vehicle:
Figure FDA0002832441450000079
the arrival time of the mobile charging vehicle is in an interval constraint:
Figure FDA0002832441450000081
the electric automobile receives service constraint:
Figure FDA0002832441450000082
the charge and discharge quantity of the mobile charging vehicle is restricted:
Figure FDA0002832441450000083
and (3) remaining power range constraint of the mobile charging vehicle:
Figure FDA0002832441450000084
electric automobile demand electric quantity restraint:
0≤RNj≤CE
wherein the content of the first and second substances,
Figure FDA0002832441450000085
respectively the earliest time point and the latest time point of an outer layer time window of the node j;
Figure FDA0002832441450000086
Figure FDA0002832441450000087
respectively the earliest and latest time points of the inner layer time window of the node j;
Figure FDA0002832441450000088
the time when the mobile charging vehicle i reaches the node j +1 is the time;
Figure FDA0002832441450000089
the time when the mobile charging vehicle i reaches the node j is the time when the mobile charging vehicle i reaches the node j; r iskDischarging power of a kth mode for charging the electric vehicle by the mobile charging vehicle; x is the number ofi,j+1For decision variables, when xi,j+1When the value is 1, the access node j +1 of the ith mobile charging vehicle is represented; when x isi,j+1When the value is 0, the ith mobile charging vehicle does not access the node j + 1;
Figure FDA00028324414500000810
for making a decision on a variable, when
Figure FDA00028324414500000811
When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when in use
Figure FDA00028324414500000812
When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement the electric energy after accessing the node j; di,j+1For mobile chargingThe distance between trolley i and node j + 1; omegaj,zFor decision variables, when ωj,zWhen the value is 1, the electric energy is supplemented at a fixed charging station z; when ω isj,zWhen the value is 0, the electric energy is not supplemented at the fixed charging station z; r isMCharging power of the mobile charging vehicle at a fixed charging station; di,zThe distance between the current position of the mobile charging vehicle and the fixed charging station z is obtained; dz,j+1The distance between the fixed charging station z and the node j + 1; v is the running speed of the mobile charging vehicle; RN (radio network node)jIs the required electric quantity of the node j; RMi,jThe electric quantity supplemented to the fixed charging station after the mobile charging vehicle i accesses the node j is obtained; lambda [ alpha ]i,j、
Figure FDA0002832441450000091
Are all decision variables, when lambdai,jThe value is 1, and the mobile charging car i is respectively shown in
Figure FDA0002832441450000092
Reaching node j within a time interval; when the decision variable is taken as 0, the mobile charging vehicle i is respectively represented to not arrive in the corresponding time interval; n is the total number of the mobile charging cars; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of nodes of the electric automobile and the nodes of the central station; q is the total number of fixed charging stations; x is the number ofi,jFor decision variables, when xi,jWhen the value is 1, the ith mobile charging vehicle access node j is represented, and when x is greater than 1i,jWhen the value is 0, the ith mobile charging vehicle does not access the node j; di,jThe distance between the mobile charging vehicle i and the node j is obtained; theta is the unit mileage energy consumption of the mobile charging vehicle; cMThe battery capacity of the mobile charging car; cEIndicating the battery capacity of the mobile charging vehicle.
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